Geophysical Joint Inversion Using Statistical Petrophysical Constraints and Prior Information

Abstract

We introduce and test a workflow that integrates petrophysical constraints and geological data in joint geophysical inversion in order to decrease the uncertainty of the results. This workflow uses statistical petrophysical properties to constrain the values retrieved by the geophysical inversion and geological prior information to decrease the effect of non-uniqueness. We integrate the different sources of information in a Bayesian framework, which takes into account the state of information. This permits us to quantify the posterior state of knowledge, the reduction of the uncertainty and to calculate the influence of prior information using quality indicators based on fixed-point statistics. This workflow was first tested using simple synthetic datasets to validate the method and assess the robustness of the workflow. As a result, the use of petrophysical constraints permits us to retrieve sharper boundaries, while prior structural information from geology permits to retrieve the geometry more accurately. Overall, the integration of the different constraints provides a model, with reduced uncertainties and better resolved parameters.